{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：13/25 | 概率：52.00%\n",
      "最近 3 年上涨月数/总月数：18/37 | 概率：48.65%\n",
      "最近 5 年上涨月数/总月数：32/61 | 概率：52.46%\n",
      "最近 8 年上涨月数/总月数：53/97 | 概率：54.64%\n",
      "最近 10 年上涨月数/总月数：67/121 | 概率：55.37%\n",
      "最近 13 年上涨月数/总月数：79/137 | 概率：57.66%\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = \"牧原\"\n",
    "year_spans = [2, 3, 5, 8, 10, 13]\n",
    "\n",
    "# ========== 获取数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "\n",
    "df = ak.stock_zh_a_daily(symbol=\"sz002714\", start_date=full_start_date, end_date=end_date, adjust=\"qfq\")\n",
    "df['date'] = pd.to_datetime(df['date'])  # 用英文字段\n",
    "df = df[['date', 'open', 'close']]  # 保留必要字段\n",
    "df = df.sort_values(\"date\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 各周期上涨统计 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"date\"] >= start_cut].copy()\n",
    "\n",
    "    # 每月开盘与收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"date\"].dt.to_period(\"M\")).agg({\n",
    "        \"open\": \"first\",\n",
    "        \"close\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"month\"] = monthly_df[\"date\"].dt.month\n",
    "    monthly_df[\"up\"] = monthly_df[\"close\"] > monthly_df[\"open\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"month\")[\"up\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"month\")[\"up\"].count()\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    # 保存当前周期上涨次数\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合并图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol} 不同周期每月上涨次数对比\")\n",
    "plt.legend(title=\"周期\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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